Healthcare is undergoing significant transformation, and digital health data is at the heart of this change. According to the Centers for Disease Control, nearly 80 percent of the nation's health care facilities have transitioned from the old paper-based system to an electronic medical record (EMR). New technologies such as smartphone applications also create new stocks of digital data. Genetic data is also growing; scientists can sequence a person's entire DNA in 24 hours and for less than 1,000. Collectively, the amount of digital health data is expected to grow from 500,000 to 25 million terabytes over the next five years.

Why do we care that our health information is now digital? How does it benefit us all?

People who work in healthcare – and in any industry for that matter – are smart, well-educated, and do their best to stay on top of the latest research, methodologies, and trends. However, it is not rational to assume that individuals have the depth of knowledge or access to data to deal with every situation they face. Furthermore, the healthcare system is already understaffed, and this problem will only worsen as the looming mass retirement of baby boomers from healthcare creates an unprecedented supply-and-demand crisis.

Digitized health data can help alleviate this troubling situation. Predictive medicine uses computing power and statistical methods to analyze EMR and other health-related data to predict clinical outcomes for individual patients. In addition to predicting health outcomes, predictive medicine can also reveal surprising and often unexpected clinical associations.

Oklahoma State University's Center for Health Systems Innovation (CHSI), through its Institute for Predictive Medicine (IPM), is a leader in the exploding field of predictive medicine thanks to Cerner Corporation's unprecedented donation of its HIPAA-compliant clinical health database, one of the largest available in the United States. Specifically, this dataset represents clinical information from more than 63 million patients and includes admission, discharge, clinical events, pharmacy and laboratory data over a period of more than 16 years.

More than two dozen full-time CHSI employees and nearly two dozen graduate students are working to fulfill CHSI's mission to transform rural and Native American health care through data analytics. Furthermore, CHSI has a number of ongoing partnerships with academia, healthcare systems and companies to drive value from digitized health data.

An example of CHSI's numerous predictive medicine projects is an effort to help physicians determine whether the performance of certain cardiovascular drugs varies by gender or race, or both. Conversely, this research will help indicate which medications perform poorly or even cause complications in these populations. Other CHSI studies aim to provide physicians with insight into whether patients with a particular disease are likely to develop or already have an associated disease, which will help manage these conditions together and lead to better health care. Another project aims to help hospitals use data on patient demographics, comorbidities, discharge status and other medical information from comprehensive EMR systems to determine whether patients are at high risk for readmission due to disease-related complications. If patients are considered high risk, they can receive the care and support needed to avoid frequent travel through the healthcare system.

Predictive medicine can also lead to the creation and implementation of tools for managing larger numbers of patients, which can help healthcare providers deal with supply-and-demand issues. For example, CHSI has developed a clinical decision support system that can detect diabetic retinopathy with a high degree of accuracy using laboratory and comorbidity data available through primary care visits. This algorithm addresses the very real challenge of low patient compliance, especially among rural and underserved populations, with annual eye exams, which are the gold standard for detecting retinopathy and preventing low vision or total vision loss. CHSI is expanding this work to other common diabetes-related microvascular complications with the goal of developing a comprehensive suite of tools that can help improve the prevention and management of these complications among the nation's growing diabetes population.

Please take the opportunity to connect and share this video with your friends and family if you find it helpful.